Torres-Cabrera, J.; Maldonado-Correa, J.; Valdiviezo-Condolo, M.; Artigao, E.; MartÃn-MartÃnez, S.; Gómez-Lázaro, E.
A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection. Appl. Sci. 2024, 14, 7458.
https://doi.org/10.3390/app14177458
AMA Style
Torres-Cabrera J, Maldonado-Correa J, Valdiviezo-Condolo M, Artigao E, MartÃn-MartÃnez S, Gómez-Lázaro E.
A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection. Applied Sciences. 2024; 14(17):7458.
https://doi.org/10.3390/app14177458
Chicago/Turabian Style
Torres-Cabrera, Joel, Jorge Maldonado-Correa, Marcelo Valdiviezo-Condolo, EstefanÃa Artigao, Sergio MartÃn-MartÃnez, and Emilio Gómez-Lázaro.
2024. "A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection" Applied Sciences 14, no. 17: 7458.
https://doi.org/10.3390/app14177458
APA Style
Torres-Cabrera, J., Maldonado-Correa, J., Valdiviezo-Condolo, M., Artigao, E., MartÃn-MartÃnez, S., & Gómez-Lázaro, E.
(2024). A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection. Applied Sciences, 14(17), 7458.
https://doi.org/10.3390/app14177458
